Abstract
Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient's survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.
Highlights
With the progress of human society, the development of science and technology, and the improvement of people’s living standards, health issues have become the focus of attention
Because of its high incidence, high mortality, low cure rate, and low survival rate, lung cancer is a malignant tumor with high mortality, making it the number one killer that threatens human survival and affects the quality of human life. e initial symptom of lung cancer is a lung nodule. e irregular shape, large gray range change, and variable scale complicate the image manifestation of pulmonary nodules, leading more researchers to evaluate the effectiveness of pulmonary nodule detection
100 100 100 artificial intelligence software detected a total of 103 true nodules 10 mm; radiologists detected a total of 19 true nodules 10 mm were detected, and the sensitivity was 90.4%. is shows that the sensitivity of artificial intelligence software to detect the size of noncalcified nodules is significantly higher than that of radiologists, and the difference is statistically significant (P < 0.05)
Summary
With the progress of human society, the development of science and technology, and the improvement of people’s living standards, health issues have become the focus of attention. Because of its high incidence, high mortality, low cure rate, and low survival rate, lung cancer is a malignant tumor with high mortality, making it the number one killer that threatens human survival and affects the quality of human life. With the development of medical imaging technology, CT imaging has gradually become one of the most popular imaging methods for diagnosing diseases. Due to the large number of slices in CT images and the abundant blood vessels and airway structures in the lungs, the diagnosis becomes more complicated and the false positive rate is higher. Due to the disadvantages of traditional artificial chest imaging in the diagnosis of chest diseases, such as heavy workload, long reading cycle, and strong subjectivity, a fast, accurate, and reproducible artificial intelligence chest CT detection system for lung nodules is required to assist doctors in diagnosing diseases
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